Competitors.
We compare the proposed \({\mathsf {SIT}}\) -based measures with 8 representative prior ones as mentioned in Section REF : (i) individual-level measures: tie strength (\({\mathsf {Tie}}\) ) [1]}, number of common neighbors (\({\mathsf {COM}}\) ) [2]}, \({\mathsf {PPR}}\)   [3]}, cosine and euclidean similarity between Node2Vec representations (\({\mathsf {N2V}}\) (\({\mathsf {cos}}\) ) and \({\mathsf {N2V}}\) (\({\mathsf {euc}}\) )) [4]}; (ii) group-level measures: structural diversity (\(\#\)\({\mathsf {CC}}\) ) [5]}, user-group tie strength (\({\mathsf {GT}}\) ) [6]} and group edge density (\({\mathsf {GD}}\) ) [7]}. All measures are computed based on the network structure as introduced in Table REF .
Notice in Tencent's MOBA and FPS online gaming platforms that the edge weight between a pair of users is described by the intimacy score, which records the number of historical activities/interactions from one to the other, e.g., co-playing, gifting, etc.
For a fair comparison, we employ intimacy values to measure the tie strength for all related \({\mathsf {SIT}}\) -based measures and competitors. Furthermore, we employ distributed frameworks in [8]} and  [9]}, [10]} to compute \({\mathsf {PPR}}\)  and \({\mathsf {N2V}}\) -based measures, respectively, and set the final embedding dimension of the latent vector to 200.
To accomplish downstream tasks, we treat our proposal called \({\mathsf {SIT}}\)  (including \(\#\)\({\mathsf {CC}}\) , \({\mathsf {GS}}\) , \({\mathsf {GPR}}\) , \({\mathsf {GPPR}}\) , \({\mathsf {UGT}}\) , and \({\mathsf {IGT}}\) ) or competing measures as input features for \({\mathsf {XGBoost}}\)  as mentioned in Section , in which the parameters are set following the original paper [11]}.
<TABLE><TABLE><TABLE>